Daily Price Research Articles

A dynamic connectedness analysis between rare earth prices and renewable energy

Current world environmental challenges put pressure on clean energy produced mostly through renewables. There is an undeniably important role of rare earth minerals in renewable energy technologies. This study aims to infer the relationship between rare earth, clean energy, renewable energy technologies, and carbon emissions, focusing on daily stock price index data and applying the novel quantile time-frequency connectedness model, and the cross-quantilogram dependence approach during 2012–2022. Results show that spillovers among rare earth minerals and renewable energy are dependent on market conditions, time horizons, and analyzed quantiles. They also highlight the net receiver role of rare earth, especially in the short term. Findings might help investors understand diversification benefits and support policymakers in developing strategies for lessening import dependence on rare earth metals, as important as they are for renewable technology adoption to ensure green growth.

From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling

Timely and reliable monitoring of food market prices at high spatial and temporal resolution is essential to understanding market and food security developments and supporting timely policy and decision-making. Mostly, decisions rely on price expectations, which are updated with new information releases. Therefore, increasing the availability and timeliness of price information has become a national and international priority. We present two new datasets in which mobile app-based crowdsourced daily price observations, voluntarily submitted by self-selected participants, are validated in real-time within spatio-temporal markets (pre-processed data). Then, they are reweighted weekly using their geo-location to resemble a formal sample design and allow for more reliable statistical inference (post-sampled data). Using real-time data collected in Nigeria, we assess the accuracy and propose that our reweighted estimates are more accurate with respect to the unweighted version. Results have important implications for governments, food chain actors, researchers and other organisations.

Predicting Indian electricity exchange‐traded market prices: SARIMA and MLP approach

AbstractThis research investigates the short‐term (ST) forecasting performance of the daily prices of the Indian exchange‐traded day‐ahead (DAM) market, divided into 13 bid areas, each consisting of states with varied fundamentals. Forecasts are built employing SARIMA (seasonal autoregressive integrated moving average) and MLP (multilayer perceptron) methods. Moreover, the robustness and performance of the model is compared using the lowest error and the Diebold–Mariano (DM) test statistic values. The results indicates that the SARIMA model has high prediction accuracy with error values ranging from 1% to 5% with Southern region having the highest error of 4.53% and Northern having the least error of 1.27%. However, validation by the DM test suggests no statistical significant difference between the two models. The power generators, distribution companies, traders, policymakers, strategists and managers could use the findings for effective power management through proper planning.

Wheat price volatility regimes over 140 years: An analysis of daily price ranges

We analyze Chicago based daily wheat price volatility over more than 140 years using a novel data set of daily high and low futures prices starting in 1877. We identify five long-run regimes and find that volatility shifts between regimes are statistically more pronounced than fluctuations within regimes, even when conditioning on economic states. Historical volatility estimates derived from average commodity price data, a common practice in empirical studies, exhibit a regime-dependent upward bias between 0% and 22%. The magnitude of the bias and the importance of regimes potentially explain contradictory findings on volatility patterns in earlier studies.

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the error function gradient, while the HyFIS approach combines the advantages of neural networks and fuzzy logic systems to create a more robust and accurate learning model. The MODWT uses five mathematical functions to form a discrete wavelet basis. The dataset used includes the daily closing prices of the Tadawul stock market from August 2011 to December 2019. Inputs were selected based on multiple regression, tolerance, and variance inflation factor tests, and the oil price (Loil) and repo rate (Repo) were identified as input variables. The output variable is represented by the logarithm of the Tadawul stock market price (LSCS). MODWT-LA8 (ARIMA(1,1,0) with drift) outperforms other WT functions on the 80% dataset, with an ME of (0.00000532), MAE of (0.003214182), and MAPE of (0.06449683). The addition of WT functions to the FS.HGD and HyFIS models increases their forecasting ability. Based on the reduced RMSE (0.048), MAE (0.038), and MAPE (0.538), the MODWT-LA8-FS.HGD outperforms traditional models in predicting the remaining 20% of datasets.

The isotropy of cryptocurrency volatility

AbstractWe examine the fractal volatility and long‐range dependence of Bitcoin, Ethereum, Tether and USD Coin by employing the continuous wavelet transform, maximal overlap discrete wavelet transform and rescaled range. Our dataset consists of daily prices spanning from January 2017 through to October 2022, encapsulating pre‐ and post‐epidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the time‐frequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the post‐epidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and long‐term fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of self‐similarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policy‐related events differ between them, as both return series move incongruously. Conditional dynamics signify that all cryptocurrencies, except Tether, were affected by the pandemic transition of COVID‐19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zero‐mean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate one‐dimensional lines, whereas stablecoins mimic two‐dimensional planes.

Analyzing the volatility spillover and cointegration relationship between daily spot West Texas intermediate crude oil price and US dollar

In the study, it is aimed to analyze the diffusion and cointegration relationship between WTI and US Dollar in the period of 2016-2021. In the study, after a comprehensive literature review of the theoretical review, the econometric analysis section was started. In the first part of the analysis, the short and long-term relationships between the variables were examined with the autoregressive distributed lag methodology and the existence of a cointegration relationship was reached. According to the findings, the effect of WTI on foreign exchange volatility in the long run is statistically significant and negative. In the short-term evaluation, ECT is negative and significant within expectations. In this context, the changes between the variables approach the long-term equilibrium level. According to the results obtained in the causality and variance causality analyzes applied in the last part of the analysis, it is understood that there is a volatility spillover effect from WTI to foreign currency.

The Estimation of Heavy Tails in Non-linear Models

A generalized student t distribution technique based on estimation of bilinear generalized autoregressive conditional heteroskedasticity (BL-GARCH) model is introduced. The paper investigates from empirical perspective, aspects of the model related to the economic and financial risk management and its impacts on volatility forecasting. The purposive sampling technique was applied to select four banks for the study, namely First Bank of Nigeria (FBN), Guaranty Trust Bank (GTB), United Bank for Africa (UBA) and Zenith Bank (ZEB). The four banks are selected, because their daily stock prices are considered to be more susceptible to volatility than those of other banks within the sampled period (January 2007–May 2022). The data collected were analyzed using MATLAB R2008b Software. The results show that the newly introduced generalized student t distribution is the most general of all the useful distributions applied in the BL-GARCH model parameter estimation. It serves as a general distribution for obtaining empirical characteristics such as volatility clustering, leptokurtosis and leverage effects between returns and conditional variances as well as capturing heavier and lighter tails in high frequency financial time series data.

Employing Recurrent Neural Networks to Forecast the Dollar Exchange Rate in the Parallel Market of Iraq

In this study, neural recurrent neural networks (RNN) have been used to forecast the price of dollars in Iraqi dinars, as it is clear that the government's efforts to control prices in the parallel markets, the commercial markets witnessed a decline in the exchange rate, but it rose again. Which indicates an economic problem that is still present in the country. Despite adjusting the exchange rate of the dinar, the dollar crisis in Iraq has not ended yet. Here we want to forecast the daily price of the dollar against Iraqi dinars for the common next 30 days. According to the results the RNN model have been performed for the data under consideration with different numbers of hidden layer and nodes. The best architecture for the RNN model was [1,10,1,1] using soft plus activation function, which gives the performance of 85% for the training dataset and (92% and 90%) for the testing and validation datasets respectively, with Mse (0.018, 0.000417, and 0.000477) for training, testing, and validation respectively at epoch 4. According to the results of the forecasted values which start from 15 May 2023 to 13 June 2023 the price of dollars will be between 1390 to 1435.

Price limit performance: New evidence from a quasi-natural experiment in China's ChiNext market

This study examines the impact of daily price limits on market performance and trading activity by using a quasi-natural experiment in China. It focuses on the case of the ChiNext market, where the daily price limits of stocks increased from 10% to 20% in 2020. The results show that, initially, the stock prices and occurrences of 10% price limit hits increase, but then decline after the new price limits have been implemented. The level of trading liquidity and volatility increases significantly, with a greater impact on the short term than the long term. These price limit performances are more pronounced for stocks with additional retail interest. The analysis of detailed trading data reveals that institutional investors initially purchase ChiNext stocks in large quantities, followed by retail investors who purchase smaller quantities. In the long run, institutional investors tend to increase their holdings, while retail investors tend to sell their holdings. Additionally, there is a temporary increase in investor attention, price synchronicity, and stock risks, followed by a decline. The findings suggest that wider price limits increase trading volumes and enhance long-term market efficiency, but encourage immediate price manipulation, causing short-term overreactions and long-term reversals. This study provides valuable insights for building an effective price limit system.

Multi-Step Vector Output Prediction of Time Series Using EMA LSTM

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with the exponential moving average (EMA) technique to reduce noise in the data and improve the accuracy of prediction. The research compares the performance of EMA LSTM to other commonly used deep learning models, including LSTM, GRU, RNN, and CNN, and evaluates the results using statistical tests. The dataset used in this study contains daily stock market prices for several years, with inputs of 60, 90, and 120 previous days, and predictions for the next 20 and 30 days. The results show that the EMA LSTM method outperforms other models in terms of accuracy, with lower RMSE and MAPE values. This study has important implications for real-world applications, such as stock market forecasting and climate prediction, and highlights the importance of careful preprocessing of the data to improve the performance of deep learning models.

Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices

Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models.

Global energy supply risk: Evidence from the reactions of European natural gas futures to Nord Stream announcements

Motivated by recent geopolitical challenges to globalization, finance scholarship has a growing interest in the risks to economies of key supply chains. We explore the effect of Nord Stream pipeline development on daily natural gas prices in Europe. Reviewing a comprehensive list of 24 announcements related to Nord Stream pipeline developments, stoppages, and sanctions for 2013–2022, we investigate their direct impact on the volatility and returns of Dutch TTF natural gas futures. Employing bootstrap quantile regression, we find that the effects of such announcements are highly significant, especially during periods of low TTF volatility and returns distributions. Moreover, results show that both COVID-19 and the Russia-Ukraine war strenghtened the correlation between Nord Stream pipeline announcements and TTF market reactions, consistent with energy markets pricing geopolitical risks. Results, reflective of informed trading, warn of concentration risk in European gas markets, suggesting the need for policy actions to reduce supply-chain dependence. Taken together, we support the importance of diversifying both natural gas supply infrastructures and providers to mitigate geopolitical risk exposures and associated welfare costs to the real economy.

Measuring the Potential Integration of Indonesian Sharia Capital Market with the OIC Member Countries

The Indonesian Composite Index (ICI) experienced a critical session, the data showed the ICI fell by 6.58% which was the biggest decline in the last 9 years. The prevailing notion was that this pressure stemmed from the prevalence of the Covid-19 concern and is anticipated to be alleviated once Covid-19 disseminates further. The purpose of this study is to determine the causality and long-term cointegration relationship between sharia stock in Indonesia and the Organization of Islamic Cooperation (OIC) member countries, as well as to determine the contribution of shares of OIC member countries, such as Egypt, Oman, Saudi Arabia, Kuwait, Turkey, Bahrain, and Malaysia in the collapse of the Islamic capital market in Indonesia. The method used in this research is quantitative by using secondary data in the form of daily prices of the Sharia index sourced from the website www.investing.com. The population of the countries related to this research is 56 members of the OIC that are active until 2021. For data analysis, several tests were conducted, such as; Testing Time Series Data, Vector Autoregressive (VAR), Granger Causality Test, Johansen Model Cointegration Test, and Contagion Effect Test. The study found that; has not been able to prove the existence of cointegration between the Indonesian Islamic stock exchange and the stock exchanges of OIC member countries other than the Malaysian stock exchange. However, this study proves the interdependence between the Malaysian and Indonesian Islamic stock exchanges, but in the long term, the contribution of the Indonesian stock exchange is greater than the Malaysian Islamic stock index. In addition, another finding was that during the Covid-19 shock, the Malaysian stock position index showed more independence, whereas the decline that occurred at the beginning of the pandemic was caused without the JII or ISSI intervention index.

Analyzing and forecasting electricity price using regime‐switching models: The case of New Zealand market

AbstractThis paper aims to study the forecasting capabilities of several models under the Markov regime‐switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with time‐varying transition probabilities and incorporation of external market variables. We apply Hamilton's filter with maximum likelihood estimation for parameter estimation. The EVT peaks‐over‐threshold (EVT‐PoT) framework is also considered, and its relationship to the MRS class of models is discussed. We generate out‐of‐sample forecasts under various market scenarios. The MRS models are able to replicate real price densities under stable market conditions. The EVT‐PoT model performs well despite its lack of complexity compared to the MRS framework. We attribute this to the usage of the generalized Pareto distribution to model price extremities.

Effectiveness of price limits: Evidence from China's ChiNext market.

Starting from August 24, 2020, the daily stock price limits in China's ChiNext market have been adjusted from 10% to 20%. We use this reform to study the effectiveness of price limits in China's stock market. We test four hypotheses about price limits: delayed price discovery, volatility spillover, trading interference, and magnet effect. Using the event study method, we examine the differences in the behavior of stock price, trading volume, and volatility before and after the reform. We confirm the delayed price discovery, volatility spillover and trading interference hypothesis of price limits, and find that these negative effects of price limits are more serious when lower limits are hit. In addition, we examine the distribution of large price movements before and after the reform and find no evidence of the magnet effect of price limits. The present research has important implications for policymakers and investors in China's stock market.

Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins

This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We compare statistical, machine learning, and deep learning forecasting models to evaluate the financial markets, such as the ARIMA, hybrid ETS-ANN, and kNN predictive models. The study results indicate that predicting financial market fluctuations is challenging, and the accuracy levels are generally low in several instances. ARIMA and hybrid ETS-ANN models perform better over extended periods compared to the kNN model, with ARIMA being the best-performing model in 2018–2021 and the hybrid ETS-ANN model being the best-performing model in most of the other subperiods. Still, the kNN model outperforms the others in several periods, depending on the observed accuracy measure. Researchers have advocated using parametric and non-parametric modeling combinations to generate better results. In this study, the results suggest that the hybrid ETS-ANN model is the best-performing model despite its moderate level of accuracy. Thus, the hybrid ETS-ANN model is a promising financial time series forecasting approach. The findings offer financial analysts an additional source that can provide valuable insights for investment decisions.

Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network

This paper presents a rigorous investigation into the multiresolution cross-correlation and causality between European energy markets and geopolitical risk (GPR). Using daily electricity spot prices from January 2015 to January 2023, the study employs the variational mode decomposition (VMD) method to uncover the underlying patterns and reveal the interaction between European power prices and GPR. The paper further develops a VMD-assisted multi-scaled causal neural network (VMD-M-CNN) as a multivariable forecasting approach, benchmarking it against other competing models. The results demonstrate a strong dependence between electricity markets and GPR across different investment time-scale horizons. Additionally, the relationship between the two has taken different forms over the crisis period in contrast with calm periods. The proposed VMD-M-CNN approach proves to be more effective than other benchmark models, providing valuable insights for market participants and policymakers to make informed decisions. The rigorous methodology employed and the novel approach developed enhance the credibility and robustness of the study, contributing to the growing body of literature on energy markets and GPR.

Electricity price forecasting in New Zealand: A comparative analysis of statistical and machine learning models with feature selection

In this study, we present an empirical comparison of statistical models and machine learning models for daily electricity price forecasting in the New Zealand electricity market. We demonstrate the effectiveness of GARCH and SV models and their t-distribution variants when paired with feature selection techniques, including LASSO, mutual information, and recursive feature elimination. A key aspect of our study is the inclusion of a diverse set of explanatory variables in all models. We compare these models against a range of popular machine learning models, including LSTM, GRU, XGBoost, LEAR, and a four-layer DNN, where the latter two are considered benchmarks. Our results reveal that GARCH and SV models, particularly their t variants, perform exceptionally well when paired with feature selection techniques and explanatory variables. In most scenarios considered, these models outperform machine learning models when coupled with LASSO feature selection. This contribution provides a comprehensive evaluation of the performance of different models and feature selection techniques for electricity price forecasting in the New Zealand electricity market. Our best-performing model improves the symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE) by 2% to 3% over the LEAR benchmark model, highlighting the practical relevance of our findings.

Is the Markowitz Model Still Effective in Forming an Optimal Stock Portfolio? Empirical Evidence from the IDX ESG Leaders Index

The current portfolio calculation was basically developed by Markowitz (1952) who gave rise to the Modern Portfolio Theory. However, along with the development of science, other models emerged in portfolio calculation. This study tried to provide a flashback to whether the Markowitz portfolio model is still effective in forming an optimal portfolio compared to other portfolio calculation models. This study used descriptive research with a quantitative approach. This study used secondary data in the form of daily stock prices of companies, including the IDX ESG Leaders Index on the Indonesia Stock Exchange for the period of December 2020 to March 2022. The results showed that 11 stocks out of 25 stock samples were included in the optimal portfolio in both the Markowitz model and the Single Index Model but with different stock compositions. In terms of portfolio performance, the Markowitz model is still superior to the Single Index Model. This can be seen from the calculation results of the three indexes used, including the Sharpe Index, Treynor Index, and Jensen Index. This study provided an important contribution that the Markowitz model portfolio formation can be used and has proven to perform well in the formation of a stock portfolio.